Categorizing ADC site page URL’s to more easily analyze user engagement.
Page: The page shows the part of the URL after your domain name (path) when someone has viewed content on your website. For example, if someone views https://www.example.com/contact then /contact will be reported as the page inside the Behavior reports.
User: An individual visitor to the site (tracked using browser cookies)
Sessions: A single visit to the website, consisting of one or more pageviews, and any other interactions (The default session timeout is 30 minutes)
User % of Total: Users displayed as a percentage of the Total Users during the report period
Pageviews: The number of times users view a page that has the Google Analytics tracking code inserted. This covers all page views; so if a user refreshes the page, or navigates away from the page and returns, these are all counted as additional page views.
Unique Pageviews: The unique pageview is the count of all the times the page was viewed in an individual session as a single event. If a user viewed the page once in their visit or five times, the number of unique pageviews will be counted as just one
Entrances: Entrance represents the number of visits that started on a specific web page or group of web pages. I.e. the first page that someone views during a session
Bounce Rate: The Bounce Rate is Bounce measured in percentage. It represents the number of visits when users leave your site after just one page view, regardless of how long they stayed on that page. (Total Bounces divided by total visits)
We will use the code and function below to categorize the Google Analytics dataset. The function takes messy character data within a dataframe and categorizes it based on a set of search string criteria. The inputs are the data frame, the column name of the messy data, a list of search strings, a list of category names (these have to be correlated), and you have the option of naming the new column.
It is important to note that the order of the search strings matters for strings that are repeats – i.e. “catalog” and “catalog/submit” will be written over so you must identify the longer string first (i.e. catalog/submit). Additionally, make sure the order of the categories list correlates with the order of the search strings.
Source: https://github.com/lenwood
Identify Search Strings and Category Names
# List of search strings -- note that the longer search strings are identified first
search <- c("news", "portals", "about","catalogprofile", "catalogsubmit", "catalog", "training", "team", "home", "view", "submit", "profile")
# List of categories
categories <- c("News", "Portals", "About", "Summary", "Submit", "Cathome", "Training", "Team", "Home", "Dataset", "WhoMustSub", "Summary")
Create Categorization Function
# Quickly categorize a data frame with a column of messy character strings.
# Replace "df" with your messy dataframe.
categorizeDF <- function(df, searchColName, searchList, catList, newColName="Category") {
# create empty data frame to hold categories
catDF <- data.frame(matrix(ncol=ncol(df), nrow=0))
colnames(catDF) <- paste0(names(df))
# add sequence so original order can be restored
df$sequence <- seq(nrow(df))
# iterate through the strings
for (i in seq_along(searchList)) {
rownames(df) <- NULL
index <- grep(searchList[i], df[,which(colnames(df) == searchColName)], ignore.case=TRUE)
tempDF <- df[index,]
tempDF$newCol <- catList[i]
catDF <- rbind(catDF, tempDF)
df <- df[-index,]
}
# OTHER category for unmatched rows
if (nrow(df) > 0) {
df$newCol <- "OTHER"
catDF <- rbind(catDF, df)
}
# return to the original order & remove the sequence data
catDF <- catDF[order(catDF$sequence),]
catDF$sequence <- NULL
# remove row names
rownames(catDF) <- NULL
# set Category type to factor
catDF$newCol <- as.factor(catDF$newCol)
# rename the new column
colnames(catDF)[which(colnames(catDF) == "newCol")] <- newColName
catDF
}
Call Function and Categorize Data
# Replace "df" with messy dataframe
# Identify which column you want to categorize -- in our case with Google Analytics, we will be categorizing the "Page" column that contains messy URL strings. Additionally, you can name the new column that contains the categories (e.g. "Category").
sorted <- categorizeDF(df, "column name with messy data", search, categories, "new category column name")
Test Run of Categorization with Small Subset of Data
###### TEST DATASET ######
# Remove backslashes and other symbols from Page column (includes hyphens and periods). **** Not sure if this is necessary. Am trying to differentiate the single "/" as the ADC Homepage, and make it easier to identify search terms for the function below.
test_users_clean <- top_30_users %>%
mutate_all(funs(gsub("[[:punct:]]", "", .)))
# Rename home page as "home" in dataframe **NOTE that for this particular dataset the "Home" page is the top viewed page and so I put in [1]. If it is not the top viewed page you will need to determine which row the homepage is and put that row number in the brackets. *** Is there a better way to do this?? ***
test_users_clean$Page[1] <- "home"
### Categorize the page URLS in the Page column into larger categories using a function ###
## Create a list of search strings to sort through pages and a list of categories (these must be correlated) **Order matters for strings that are repeats -- i.e. "catalog" and "catalog/submit" will be written over so you must identify the longer string first (i.e. catalog/submit).
# List of search strings
search <- c("news", "portals", "about","catalogprofile", "catalogsubmit", "catalog", "training", "team", "home", "view", "submit", "profile")
# List of categories
categories <- c("News", "Portals", "About", "Summary", "Submit", "Cathome", "Training", "Team", "Home", "Dataset", "WhoMustSub", "Summary")
## Create function [below] to categorize the messy "Page" column of the raw data frame.
# This function takes looks at a data frame column of messy character (or factorial) data, and produces a new column of categorized data. The inputs are the data frame, the column name of the messy data, a list of search strings, a list of category names (these two have to be correlated), and you have the option of naming the new column.
# Function:
categorizeDF <- function(test_users_clean, searchColName, searchList, catList, newColName="Category") {
# create empty data frame to hold categories
catDF <- data.frame(matrix(ncol=ncol(test_users_clean), nrow=0))
colnames(catDF) <- paste0(names(test_users_clean))
# add sequence so original order can be restored
test_users_clean$sequence <- seq(nrow(test_users_clean))
# iterate through the strings
for (i in seq_along(searchList)) {
rownames(test_users_clean) <- NULL
index <- grep(searchList[i], test_users_clean[,which(colnames(test_users_clean) == searchColName)], ignore.case=TRUE)
tempDF <- test_users_clean[index,]
tempDF$newCol <- catList[i]
catDF <- rbind(catDF, tempDF)
test_users_clean <- test_users_clean[-index,]
}
# OTHER category for unmatched rows
if (nrow(test_users_clean) > 0) {
test_users_clean$newCol <- "OTHER"
catDF <- rbind(catDF, test_users_clean)
}
# return to the original order & remove the sequence data
catDF <- catDF[order(catDF$sequence),]
catDF$sequence <- NULL
# remove row names
rownames(catDF) <- NULL
# set Category type to factor
catDF$newCol <- as.factor(catDF$newCol)
# rename the new column
colnames(catDF)[which(colnames(catDF) == "newCol")] <- newColName
catDF
}
# Call the function and create new data frame - using the raw data frame, the messy column you want to sort, the search and category lists, and name of the new column
sortedDF <- categorizeDF(test_users_clean, "Page", search, categories, "Category")
knitr::kable(sortedDF, format = "html")
| Page | Users | Sessions | Users_._of_Total | Pageviews | Unique_Pageviews | Entrances | Bounce_Rate | Category |
|---|---|---|---|---|---|---|---|---|
| home | 25436 | 42464 | 0440145 | 75951 | 55359 | 42443 | 0421957423 | Home |
| catalog | 4310 | 2280 | 0257363 | 12070 | 8734 | 2131 | 0247368421 | Cathome |
| catalog | 4130 | 416 | 0195397 | 33 | 26 | 19 | 0033653846 | Cathome |
| data | 3291 | 2306 | 0160785 | 19380 | 9507 | 2319 | 0273200347 | OTHER |
| catalogdata | 3114 | 3395 | 0139405 | 14923 | 7964 | 3298 | 0253608247 | Cathome |
| about | 2637 | 941 | 0123776 | 3942 | 3297 | 944 | 0582359192 | About |
| team | 1634 | 614 | 0110133 | 2554 | 2117 | 615 | 0684039088 | Team |
| submit | 1384 | 898 | 009936 | 3255 | 2395 | 901 | 0643652561 | WhoMustSub |
| page0 | 1174 | 1580 | 0090577 | 5362 | 3813 | 1582 | 0158860759 | OTHER |
| training | 1166 | 892 | 0083537 | 2245 | 1639 | 892 | 0515695067 | Training |
| publications | 1120 | 431 | 0077705 | 1701 | 1326 | 432 | 0744779582 | OTHER |
| share | 1060 | 528 | 0072758 | 4532 | 2706 | 529 | 0357954545 | OTHER |
| profile | 989 | 232 | 0068477 | 1609 | 1338 | 238 | 061637931 | Summary |
| qanda | 912 | 214 | 0064713 | 1430 | 1181 | 214 | 0570093458 | OTHER |
| january2019datasciencetrainingforarcticresearchers | 903 | 1004 | 0061441 | 1359 | 1191 | 1004 | 0815737052 | Training |
| datapage0 | 873 | 556 | 0058545 | 4704 | 2254 | 557 | 0303956835 | OTHER |
| catalogprofile | 799 | 193 | 0055914 | 1376 | 1121 | 188 | 0564766839 | Summary |
| proposals | 773 | 660 | 0053551 | 1187 | 1008 | 661 | 0762121212 | OTHER |
| homehtm | 735 | 800 | 0051402 | 936 | 817 | 800 | 056875 | Home |
| support | 729 | 121 | 0049463 | 1308 | 1004 | 122 | 058677686 | OTHER |
| dataplans | 685 | 384 | 0047672 | 932 | 827 | 384 | 0841145833 | OTHER |
| 2018datasciencetrainingforarcticresearchers | 649 | 639 | 0046015 | 982 | 876 | 639 | 0723004695 | Training |
| news201606datascienceopportunities | 629 | 371 | 0044488 | 810 | 733 | 371 | 0851752022 | News |
| upcomingdatasciencetrainingforarcticresearchers | 599 | 612 | 0043066 | 857 | 767 | 612 | 0823529412 | Training |
| catalogsubmit | 582 | 302 | 0041746 | 1657 | 1196 | 304 | 0523178808 | Submit |
| catalogportalspermafrost | 548 | 651 | 0040505 | 874 | 722 | 650 | 0769585253 | Portals |
| reconcilinghistoricalandcontemporarytrendsinterrestrialcarbonexchangeofthenorthernpermafrostzone | 546 | 844 | 0039355 | 1189 | 995 | 844 | 0808056872 | OTHER |
| viewdoi103334CDIAC00001V2017 | 522 | 562 | 0038272 | 769 | 619 | 562 | 0807829181 | Dataset |
| catalogshare | 521 | 399 | 0037263 | 1197 | 994 | 378 | 0483709273 | Cathome |
| categorynews | 512 | 197 | 0036317 | 822 | 672 | 197 | 0624365482 | News |
Visualizations for User Analysis
## 2016 2017 2018 2019 2020
## 23772 32205 48337 31187 60330
## `summarise()` ungrouping output (override with `.groups` argument)
## $tm
## Year Category vSize vColor stdErr vColorValue level x0
## 1 2016 About 366 3 368 NA 2 0.9307865
## 2 2016 API 11 3 13 NA 2 0.9809736
## 3 2016 Cathome 3022 3 3024 NA 2 0.9119412
## 4 2016 DataPlans 93 3 95 NA 2 0.9543366
## 5 2016 DataPreservation 3 3 5 NA 2 0.9982011
## 6 2016 Dataset 12211 3 12213 NA 2 0.8074770
## 7 2016 History 85 3 87 NA 2 0.9809736
## 8 2016 Home 3585 3 3587 NA 2 0.8074770
## 9 2016 KNB 11 3 13 NA 2 0.9934042
## 10 2016 MyData 178 3 180 NA 2 0.9307865
## 11 2016 <NA> 23772 25 23774 NA 1 0.8074770
## 12 2016 News 880 3 882 NA 2 0.8701616
## 13 2016 OTHER 1613 3 1615 NA 2 0.8074770
## 14 2016 Outage 1 3 3 NA 2 0.9967021
## 15 2016 Projects 38 3 40 NA 2 0.9809736
## 16 2016 Publications 170 3 172 NA 2 0.9776289
## 17 2016 QA 199 3 201 NA 2 0.9307865
## 18 2016 SearchTips 8 3 10 NA 2 0.9934042
## 19 2016 Summary 680 3 682 NA 2 0.8701616
## 20 2016 Support 131 3 133 NA 2 0.9543366
## 21 2016 Team 307 3 309 NA 2 0.9684271
## 22 2016 Training 1 3 3 NA 2 0.9983510
## 23 2016 Webinar 2 3 4 NA 2 0.9934042
## 24 2016 WhoMustSub 177 3 179 NA 2 0.9543366
## 25 2017 About 549 3 551 NA 2 0.8936335
## 26 2017 API 31 3 33 NA 2 0.9876467
## 27 2017 Cathome 1690 3 1692 NA 2 0.8248908
## 28 2017 DataPlans 106 3 108 NA 2 0.9869822
## 29 2017 Dataset 19535 3 19537 NA 2 0.5549019
## 30 2017 History 98 3 100 NA 2 0.9667187
## 31 2017 Home 4273 3 4275 NA 2 0.8248908
## 32 2017 KNB 46 3 48 NA 2 0.9667187
## 33 2017 MyData 184 3 186 NA 2 0.9426230
## 34 2017 <NA> 32205 24 32207 NA 1 0.5549019
## 35 2017 News 662 3 664 NA 2 0.8936335
## 36 2017 OTHER 2044 3 2046 NA 2 0.9433397
## 37 2017 Projects 38 3 40 NA 2 0.9876467
## 38 2017 Proposals 302 3 304 NA 2 0.9189513
## 39 2017 Publications 221 3 223 NA 2 0.9752820
## 40 2017 QA 178 3 180 NA 2 0.9426230
## 41 2017 SearchTips 14 3 16 NA 2 0.9876467
## 42 2017 Summary 804 3 806 NA 2 0.8248908
## 43 2017 Support 165 3 167 NA 2 0.9667187
## 44 2017 Team 323 3 325 NA 2 0.8936335
## 45 2017 Training 648 3 650 NA 2 0.9473851
## 46 2017 Webinar 2 3 4 NA 2 0.9984558
## 47 2017 WhoMustSub 292 3 294 NA 2 0.9426230
## 48 2018 About 627 3 629 NA 2 0.4837325
## 49 2018 API 21 3 23 NA 2 0.5383027
## 50 2018 Cathome 2454 3 2456 NA 2 0.3039637
## 51 2018 DataPlans 147 3 149 NA 2 0.5080145
## 52 2018 Dataset 26478 3 26480 NA 2 0.0000000
## 53 2018 History 105 3 107 NA 2 0.5383027
## 54 2018 Home 7559 3 7561 NA 2 0.3039637
## 55 2018 KNB 4 3 6 NA 2 0.5454167
## 56 2018 MyData 170 3 172 NA 2 0.5080145
## 57 2018 <NA> 48337 24 48339 NA 1 0.0000000
## 58 2018 News 450 3 452 NA 2 0.5251654
## 59 2018 OTHER 3828 3 3830 NA 2 0.4705433
## 60 2018 Projects 16 3 18 NA 2 0.5454167
## 61 2018 Proposals 403 3 405 NA 2 0.4837325
## 62 2018 Publications 211 3 213 NA 2 0.4837325
## 63 2018 QA 180 3 182 NA 2 0.4837325
## 64 2018 SearchTips 8 3 10 NA 2 0.5517402
## 65 2018 Submit 134 3 136 NA 2 0.5080145
## 66 2018 Summary 1324 3 1326 NA 2 0.4093999
## 67 2018 Support 150 3 152 NA 2 0.5329235
## 68 2018 Team 344 3 346 NA 2 0.5221278
## 69 2018 Training 1946 3 1948 NA 2 0.3039637
## 70 2018 WhoMustSub 1778 3 1780 NA 2 0.4093999
## 71 2019 About 588 3 590 NA 2 0.7034105
## 72 2019 API 2 3 4 NA 2 0.8038399
## 73 2019 Blog 108 3 110 NA 2 0.7746135
## 74 2019 Cathome 4522 3 4524 NA 2 0.5549019
## 75 2019 DataPlans 223 3 225 NA 2 0.7790748
## 76 2019 Dataset 14501 3 14503 NA 2 0.5549019
## 77 2019 History 103 3 105 NA 2 0.7914346
## 78 2019 Home 5289 3 5291 NA 2 0.5549019
## 79 2019 KNB 1 3 3 NA 2 0.8062646
## 80 2019 <NA> 31187 25 31189 NA 1 0.5549019
## 81 2019 News 346 3 348 NA 2 0.7034105
## 82 2019 OTHER 1893 3 1895 NA 2 0.7034105
## 83 2019 Portals 442 3 444 NA 2 0.7034105
## 84 2019 Projects 45 3 47 NA 2 0.7941411
## 85 2019 Proposals 82 3 84 NA 2 0.7746135
## 86 2019 Publications 245 3 247 NA 2 0.7478707
## 87 2019 QA 157 3 159 NA 2 0.7478707
## 88 2019 SearchTips 8 3 10 NA 2 0.7941411
## 89 2019 Submit 732 3 734 NA 2 0.7784573
## 90 2019 Summary 530 3 532 NA 2 0.7034105
## 91 2019 Support 127 3 129 NA 2 0.7478707
## 92 2019 Team 340 3 342 NA 2 0.7478707
## 93 2019 Training 577 3 579 NA 2 0.7559351
## 94 2019 WhoMustSub 326 3 328 NA 2 0.7783003
## 95 2020 About 615 3 617 NA 2 0.4478588
## 96 2020 API 2 3 4 NA 2 0.5475081
## 97 2020 Blog 701 3 703 NA 2 0.5056649
## 98 2020 Cathome 5783 3 5785 NA 2 0.4461653
## 99 2020 DataPlans 129 3 131 NA 2 0.5319809
## 100 2020 Dataset 36322 3 36324 NA 2 0.0000000
## 101 2020 History 84 3 86 NA 2 0.5319809
## 102 2020 Home 5961 3 5963 NA 2 0.3340817
## 103 2020 KNB 1 3 3 NA 2 0.5530535
## 104 2020 MyData 2 3 4 NA 2 0.5530535
## 105 2020 <NA> 60330 25 60332 NA 1 0.0000000
## 106 2020 News 1132 3 1134 NA 2 0.4478588
## 107 2020 OTHER 1814 3 1816 NA 2 0.3340817
## 108 2020 Portals 2815 3 2817 NA 2 0.3340817
## 109 2020 Projects 28 3 30 NA 2 0.5475081
## 110 2020 Publications 295 3 297 NA 2 0.5278539
## 111 2020 QA 217 3 219 NA 2 0.4940209
## 112 2020 SearchTips 7 3 9 NA 2 0.5475081
## 113 2020 Submit 1690 3 1692 NA 2 0.3929834
## 114 2020 Summary 823 3 825 NA 2 0.4478588
## 115 2020 Support 202 3 204 NA 2 0.4940209
## 116 2020 Team 398 3 400 NA 2 0.4478588
## 117 2020 Training 940 3 942 NA 2 0.5063399
## 118 2020 WhoMustSub 369 3 371 NA 2 0.4940209
## y0 w h color
## 1 0.081746241 0.037640601 0.049652726 #035C47
## 2 0.000000000 0.012430589 0.004518763 #035C48
## 3 0.131398967 0.088058800 0.175242829 #035C49
## 4 0.000000000 0.026636977 0.017828572 #035C4B
## 5 0.003096774 0.001798861 0.008516129 #035C4C
## 6 0.306641796 0.192522995 0.323882274 #035C4D
## 7 0.020129033 0.019026412 0.022812904 #035C4F
## 8 0.131398967 0.104464195 0.175242829 #035C50
## 9 0.011612904 0.006595823 0.008516129 #035C51
## 10 0.000000000 0.023550065 0.038596368 #035C53
## 11 0.000000000 0.192522995 0.630524070 #146660
## 12 0.057276473 0.060624924 0.074122494 #035C54
## 13 0.000000000 0.062684617 0.131398967 #035C55
## 14 0.000000000 0.001648956 0.003096774 #035C57
## 15 0.004518763 0.012430589 0.015610271 #035C58
## 16 0.042941938 0.022371113 0.038804303 #035C59
## 17 0.038596368 0.023550065 0.043149873 #035C5B
## 18 0.003096774 0.004796962 0.008516129 #035C5C
## 19 0.000000000 0.060624924 0.057276473 #035A5C
## 20 0.017828572 0.026636977 0.025113365 #03595C
## 21 0.081746241 0.031572854 0.049652726 #03585C
## 22 0.000000000 0.001648956 0.003096774 #03565C
## 23 0.000000000 0.003297911 0.003096774 #03555C
## 24 0.042941938 0.023292276 0.038804303 #03545C
## 25 0.695671212 0.048989523 0.057225250 #007B5B
## 26 0.637137923 0.012353328 0.012814341 #007B5D
## 27 0.690247946 0.068742705 0.125538995 #007B5F
## 28 0.665660165 0.013017786 0.041580270 #007B61
## 29 0.630524070 0.269988846 0.369475930 #007B63
## 30 0.641748100 0.020927992 0.023912065 #007B65
## 31 0.815786941 0.118448902 0.184213059 #007B67
## 32 0.630524070 0.020927992 0.011224030 #007B69
## 33 0.668246481 0.024095676 0.038993953 #007B6B
## 34 0.630524070 0.445098069 0.369475930 #16897E
## 35 0.752896461 0.053751630 0.062890480 #007B6D
## 36 0.815786941 0.056660322 0.184213059 #007B6F
## 37 0.649952263 0.012353328 0.015707901 #007B70
## 38 0.630524070 0.023671737 0.065147142 #007B72
## 39 0.707240435 0.024717965 0.045656027 #007B74
## 40 0.630524070 0.024095676 0.037722411 #007B76
## 41 0.630524070 0.010809162 0.006613853 #007B78
## 42 0.630524070 0.068742705 0.059723877 #007B7A
## 43 0.665660165 0.020263534 0.041580270 #007A7B
## 44 0.630524070 0.025317785 0.065147142 #00787B
## 45 0.752896461 0.052614889 0.062890480 #00767B
## 46 0.630524070 0.001544166 0.006613853 #00757B
## 47 0.707240435 0.032659031 0.045656027 #00737B
## 48 0.135823689 0.041432915 0.077275284 #56E47A
## 49 0.000000000 0.007113948 0.015073954 #56E47C
## 50 0.094247864 0.105436232 0.118851110 #56E47F
## 51 0.022591773 0.030288171 0.024783512 #56E481
## 52 0.000000000 0.303963699 0.444817654 #56E483
## 53 0.015073954 0.016599211 0.032301330 #56E485
## 54 0.213098974 0.166579617 0.231718680 #56E488
## 55 0.000000000 0.009485263 0.002153422 #56E48A
## 56 0.047375285 0.024908922 0.034850784 #56E48C
## 57 0.000000000 0.554901931 0.444817654 #7AFDB1
## 58 0.135823689 0.029736542 0.077275284 #56E48E
## 59 0.213098974 0.084358615 0.231718680 #56E490
## 60 0.002153422 0.006323509 0.012920532 #56E493
## 61 0.082226069 0.038395302 0.053597621 #56E495
## 62 0.037853433 0.024282075 0.044372636 #56E497
## 63 0.000000000 0.024282075 0.037853433 #56E499
## 64 0.002153422 0.003161754 0.012920532 #56E49C
## 65 0.000000000 0.030288171 0.022591773 #56E49E
## 66 0.000000000 0.074332543 0.090955204 #56E4A0
## 67 0.047375285 0.021978460 0.034850784 #56E4A2
## 68 0.082226069 0.032774154 0.053597621 #56E4A5
## 69 0.000000000 0.105436232 0.094247864 #56E4A7
## 70 0.090955204 0.074332543 0.122143770 #56E4A9
## 71 0.151378004 0.052524551 0.057165438 #64D443
## 72 0.000000000 0.002424713 0.004211999 #61D443
## 73 0.021442904 0.016821137 0.032785889 #5FD443
## 74 0.000000000 0.148508573 0.155488256 #5DD443
## 75 0.054228793 0.028402158 0.040093325 #5BD443
## 76 0.337349685 0.252575074 0.293174385 #58D443
## 77 0.021442904 0.016042381 0.032785889 #56D443
## 78 0.155488256 0.148508573 0.181861429 #54D443
## 79 0.000000000 0.001212356 0.004211999 #52D443
## 80 0.000000000 0.252575074 0.630524070 #6DEC65
## 81 0.000000000 0.044460178 0.039739597 #50D443
## 82 0.208543442 0.075046814 0.128806243 #4DD443
## 83 0.039739597 0.044460178 0.050765613 #4BD443
## 84 0.004211999 0.013335920 0.017230905 #49D443
## 85 0.000000000 0.019527598 0.021442904 #47D443
## 86 0.054228793 0.031204165 0.040093325 #45D443
## 87 0.024250200 0.026742805 0.029978593 #43D443
## 88 0.000000000 0.009698851 0.004211999 #43D445
## 89 0.208543442 0.029019687 0.128806243 #43D447
## 90 0.090505210 0.044460178 0.060872794 #43D449
## 91 0.000000000 0.026742805 0.024250200 #43D44C
## 92 0.094322118 0.030429654 0.057055886 #43D44E
## 93 0.151378004 0.051541949 0.057165438 #43D450
## 94 0.094322118 0.029176669 0.057055886 #43D452
## 95 0.488844400 0.046162045 0.068031279 #132247
## 96 0.444817654 0.005545400 0.001841686 #132247
## 97 0.556875679 0.049237009 0.072701758 #132147
## 98 0.728421298 0.108736673 0.271578702 #132047
## 99 0.472442950 0.022920986 0.028739219 #131F47
## 100 0.444817654 0.334081683 0.555182346 #131E47
## 101 0.444817654 0.015527120 0.027625296 #131E47
## 102 0.728421298 0.112083574 0.271578702 #131D47
## 103 0.444817654 0.001848467 0.002762530 #131C47
## 104 0.447580184 0.001848467 0.005525059 #131B47
## 105 0.444817654 0.554901931 0.555182346 #1F254F
## 106 0.629577438 0.058481067 0.098843861 #131B47
## 107 0.444817654 0.058901763 0.157263360 #131A47
## 108 0.602081014 0.113777165 0.126340285 #131947
## 109 0.453105243 0.007393867 0.019337707 #131847
## 110 0.501182170 0.027048052 0.055693510 #131747
## 111 0.471991000 0.037960052 0.029191169 #131747
## 112 0.446659340 0.005545400 0.006445902 #131647
## 113 0.444817654 0.054875402 0.157263360 #131547
## 114 0.556875679 0.057806075 0.072701758 #131447
## 115 0.444817654 0.037960052 0.027173346 #131347
## 116 0.444817654 0.046162045 0.044026746 #141347
## 117 0.629577438 0.048562017 0.098843861 #151347
## 118 0.501182170 0.033832987 0.055693510 #151347
##
## $type
## [1] "index"
##
## $vSize
## [1] "Users"
##
## $vColor
## [1] NA
##
## $stdErr
## [1] "Users"
##
## $algorithm
## [1] "pivotSize"
##
## $vpCoorX
## [1] 0.02812148 0.97187852
##
## $vpCoorY
## [1] 0.01968504 0.91031496
##
## $aspRatio
## [1] 1.483512
##
## $range
## [1] NA
##
## $mapping
## [1] NA NA NA
##
## $draw
## [1] TRUE